GPflow is an open-source Python library designed for building and deploying Gaussian process models. It leverages TensorFlow for scalable data flow graphs and automatic differentiation, which allows users to perform efficient and flexible probabilistic modeling with Gaussian processes. GPflow is particularly suited for tasks that involve regression and classification problems, where it offers capabilities to handle small to medium-sized datasets effectively. The library supports various kernel functions and features an intuitive API, making it accessible for both researchers and practitioners aiming to deploy Gaussian process models in real-world applications.